Recommender systems have witnessed impressive gains in terms of research methodology and practical success in the past few years (e.g., Amazon and Netflix services). One of the most popular recommendation strategies is collaborative filtering (CF), also known as user-based strategy, which relies on an intuitive observation: users who liked similar items in the past, are likely to agree in the future as well. Given a user, the CF strategy recommends new items which are popular among the set of users who are similar to a given user.
The results returned by recommender systems can, however, suffer from over-specialization, which is to say, the items returned by a recommender system are often similar, or identical, to those previously rated by the user, and while relevant, may be uninteresting. This problem is primarily caused by the fact that recommendation systems focus on maximizing the expected rating, i.e., relevance, of recommended items, while ignoring the novelty aspect, i.e. items that might be more interesting for the user to discover.